Patch Denoising Methods

This repository implements patch-denoising methods, with a particular focus on local-low rank methods.

The target application is functional MRI thermal noise removal, but this methods can be applied to a wide range of image modalities.

It includes several local-low-rank based denoising methods (see the documentation for more details):

1. MP-PCA
2. Hybrid-PCA
3. NORDIC
4. Optimal Thresholding
5. Raw Singular Value Thresholding

A mathematical description of these methods is available in the documentation.

Installation

$ pip install patch-denoise

patch-denoise requires Python>=3.9

Quickstart

After installing you can use the patch-denoise command-line.

$ patch-denoise input_file.nii output_file.nii --mask="auto"

See patch-denoise --help for detailed options.

Documentation and Examples

Documentation and examples are available at https://paquiteau.github.io/patch-denoising/

Development version

$ git clone https://github.com/paquiteau/patch-denoising
$ pip install -e patch-denoising[dev,doc,test,optional]

Citation

If you use this package for academic work, please cite the associated publication, available on HAL

@inproceedings{comby2023,
  TITLE = {{Denoising of fMRI volumes using local low rank methods}},
  AUTHOR = {Pierre-Antoine, Comby and Zaineb, Amor and Alexandre, Vignaud and Philippe, Ciuciu},
  URL = {https://hal.science/hal-03895194},
  BOOKTITLE = {{ISBI 2023 - International Symposium on Biomedical Imaging 2023}},
  ADDRESS = {Carthagena de India, Colombia},
  YEAR = {2023},
  MONTH = Apr,
  KEYWORDS = {functional MRI ; patch denoising ; singular value thresholding ; functional MRI patch denoising singular value thresholding},
  PDF = {https://hal.science/hal-03895194/file/isbi2023_denoise.pdf},
  HAL_ID = {hal-03895194},
  HAL_VERSION = {v1},
}

Related Packages

• https://github.com/paquiteau/retino-pypeline

  For the application of the denoising in an fMRI pypeline using Nipype

• https://github.com/CEA-COSMIC/ModOpt

  For the integration of the patch-denoising in convex optimisation algorithms.